Global gross primary production from vegetation photosynthesis model for 2000-2016
type: Dataset
creator: Yao Zhang
creator: Xiangming Xiao
  type: Person
  family name: Xiao
  given name: Xiangming
creator: Xiaocui Wu
  type: Person
  family name: Wu
  given name: Xiaocui
creator: Sha Zhou
creator: Geli Zhang
  type: Person
  family name: Zhang
  given name: Geli
creator: Yuanwei Qin
creator: Jinwei Dong
publisher: PANGAEA
  type: Organization
  disambiguating description: Data Publisher for Earth & Environmental Science
included in data catalog: PANGAEA
  type: DataCatalog
  disambiguating description: Data Publisher for Earth & Environmental Science
date published: 2017-08-12
description: Accurate estimation of the gross primary production (GPP) of terrestrial vegetation is vital for understanding of the global carbon cycle and predicting the future climate change. Multiple GPP products are currently available based on different methods, but their performances vary substantially when validated against GPP estimates from eddy covariance data. This paper provides a new GPP dataset at moderate spatial (500 m) and temporal (8-day) resolutions over the entire globe for 2000-2016. This GPP dataset is based on an improved light use efficiency theory and is driven by satellite data from MODIS and climate data from NCEP Reanalysis II. It also employs a state-of-the-art vegetation index (VI) gap-filling and smoothing algorithm and a separate treatment for C3/C4 photosynthesis pathways. All these improvements aim to solve several critical problems existing in current GPP products. […]
abstract: Accurate estimation of the gross primary production (GPP) of terrestrial vegetation is vital for understanding of the global carbon cycle and predicting the future climate change. Multiple GPP products are currently available based on different methods, but their performances vary substantially when validated against GPP estimates from eddy covariance data. This paper provides a new GPP dataset at moderate spatial (500 m) and temporal (8-day) resolutions over the entire globe for 2000-2016. This GPP dataset is based on an improved light use efficiency theory and is driven by satellite data from MODIS and climate data from NCEP Reanalysis II. It also employs a state-of-the-art vegetation index (VI) gap-filling and smoothing algorithm and a separate treatment for C3/C4 photosynthesis pathways. All these improvements aim to solve several critical problems existing in current GPP products. With a satisfactory performance when validated against in situ GPP estimates, this dataset offers an alternative GPP estimates for regional to global carbon cycle studies.
citation: Figure 4. Spatial pattern of annual and maximum daily GPP for 2000-2016
temporal coverage: 2000-01-01T00:00:00/2016-12-31T23:59:00
size: QuantitativeValue
  value: 2.0
  unit text: datasets
license: https://creativecommons.org/licenses/by/3.0/
conditions of access: unrestricted
is accessible for free: true
language: en
distribution: DataDownload
  encoding format: application/zip

Yao Zhang
type: Person
family name: Zhang
given name: Yao

Sha Zhou
type: Person
family name: Zhou
given name: Sha

Yuanwei Qin
type: Person
family name: Qin
given name: Yuanwei

Jinwei Dong
type: Person
family name: Dong
given name: Jinwei

Figure 4. Spatial pattern of annual and maximum daily GPP for 2000-2016
type: CreativeWork
type: WebPage

A global moderate resolution dataset of gross primary production of vegetation for 2000–2016
type: CreativeWork
type: PublicationIssue
creator: Yao Zhang
creator: Xiangming Xiao
  type: Person
  family name: Xiao
  given name: Xiangming
creator: Xiaocui Wu
  type: Person
  family name: Wu
  given name: Xiaocui
creator: Sha Zhou
creator: Geli Zhang
  type: Person
  family name: Zhang
  given name: Geli
creator: Yuanwei Qin
creator: Jinwei Dong
date published: 2017
issue number: 4(1)
pagination: 170165
is part of: Scientific Data
is based on: Global gross primary production from vegetation photosynthesis model for 2000-2016